Linking Health News to Research Literature

• URL: http://arxiv.org/abs/2107.06472v1
• Date: Wed, 14 Jul 2021 03:50:51 GMT
• Title: Linking Health News to Research Literature
• Authors: Jun Wang, Bei Yu
• Abstract summary: Accurately linking news articles to scientific research works is a critical component in a number of applications. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem.
• Score: 12.80865601729801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem. In this paper we designed and evaluated a new approach that consists of (1) augmenting latest named-entity recognition techniques to extract various metadata, and (2) designing a new elastic search engine that can facilitate the use of enriched metadata queries. To evaluate our approach, we constructed two datasets of paired news articles and research papers: one is used for training models to extract metadata, and the other for evaluation. Our experiments showed that the new approach performed significantly better than a baseline approach used by altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate the effectiveness of the approach, we also conducted a study on 37,600 health-related press releases published on EurekAlert!, which showed that our approach was able to identify the corresponding research papers with a top-1 accuracy of at least 0.97.

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